Abstract

African easterly waves (AEWs) can act as seed disturbances for tropical cyclones (TCs) in the Atlantic, and changes in future AEW activity may have important consequences for development of TCs. The simulation of AEWs was investigated using output from phase 5 of the Coupled Model Intercomparison Project (CMIP5) suite of experiments, including coupled historical and future simulations and atmosphere only (AMIP) simulations. Large biases exist in the simulation of low- (850 hPa) and midlevel (700 hPa) eddy kinetic energy (EKE, a proxy for AEW activity) in AMIP and historical simulations. CMIP5 models simulate excessive EKE and deficient rainfall south of 17°N. The same biases exist in historical and AMIP models and are not a consequence of errors in sea surface temperatures. The models also struggle to accurately couple AEWs and rainfall, with little improvement from CMIP3 models. CMIP5 models are unable to propagate AEWs across the coast and into the Atlantic, which is shown to be related to the resolution of the Guinea Highlands. Future projections of the annual cycle of AEW activity show a reduction in late spring and early summer and a large increase between July and October that is consistent with rainfall projections in the Sahel, but large differences exists in future projections between high- and low-resolution models. The simulation of AEWs is challenging for CMIP5 models and must be further diagnosed in order to accurately predict future TC activity and rainfall in the Sahel.

1. Introduction

African easterly waves (AEWs) are the dominant synoptic weather systems to impact rainfall across the Sahel during the West African monsoon (WAM; see Kiladis et al. 2006, and references therein). In addition to impacting Sahel rainfall, AEWs play an important role in Atlantic tropical cyclone (TC) development. While not all AEWs lead to the formation of a TC, approximately 60% of all Atlantic TCs and 80% of all major hurricanes form in association with AEWs (Landsea 1993; Pasch et al. 1998). It has been suggested that variability in AEW activity (number, intensity, physical, and dynamical characteristics) impacts TCs, as AEWs can act as preexisting cyclonic circulations or seeds for Atlantic TCs (e.g., Burpee 1972; Avila and Pasch 1992; Thorncroft and Hodges 2001) and vary on time scales from days to decades (e.g., Carlson 1969; Hopsch et al. 2007; Martin and Thorncroft 2014). Thus, if we are to make accurate projections of future Atlantic TC changes, we should consider changes in AEWs as well as changes in the environmental conditions, for example, sea surface temperature (SST), vertical wind shear, and humidity. As current projections of future TC activity in the Atlantic do not show a consensus (e.g., Camargo 2013), understanding AEW variability and trends could improve uncertainty. For example, the downscaling work of Emanuel (2013) assumes a constant, random input of TC seedlings in the future and shows increases in frequency and intensity of TCs in the future. However, the stationarity of seeds assumption has not been evaluated.

AEWs are westward-moving systems with wavelengths of 2000–4000 km, periods of 2–10 days and peak amplitudes close to the level of the African easterly jet (AEJ; 600–700 hPa). They are triggered by upstream convection and develop through mixed baroclinic–barotropic processes along the AEJ (e.g., Burpee 1972; Kiladis et al. 2006; Thorncroft et al. 2008). The coastal enhancement of AEW activity and rainfall has previously been shown to be important for downstream development of TCs (Berry and Thorncroft 2005; Hopsch et al. 2010; Ventrice et al. 2012).

AEW vorticity anomalies cluster in two tracks, one to the north of the AEJ around 20°N and one to the south around 10°N (Reed et al. 1988; Chen 2006; Pytharoulis and Thorncroft 1999). Thorncroft and Hodges (2001), Chen (2006), and Chen et al. (2008) argue that the northern track is less efficient for tropical cyclogenesis than the southern track. Indeed, Hopsch et al. (2007) showed that the southern track provides most of the storms that reach the Atlantic main development region. This is suggested by Fig. 1, which shows 30-yr mean summertime [June–September (JJAS)] 850- (Fig. 1a) and 700-hPa (Fig. 1b) eddy kinetic energy (EKE; see section 2 for details) and TC genesis locations. It is evident that most TC genesis occurs downstream of the southern track, although this does not eliminate the possibility of northern track vortices tracking southward after leaving the coast and leading to TC genesis. A recent study by Skinner and Diffenbaugh (2014) investigating future projections of AEW activity using phase 5 of the Coupled Model Intercomparison Project (CMIP5) output showed a significant increase in JJAS EKE activity over land in the northern track. However, as discussed, this track is not currently as effective for TC development as the southern track. Errors in the representation of TCs, AEWs, and associated precipitation in previous generations of models have been noted by Ruti and Dell’Aquila (2010), Daloz et al. (2012), and Skinner and Diffenbaugh (2013) but have not been fully understood.

Fig. 1.

JJAS EKE at (a) 850 and (b) 700 hPa from an average of four reanalysis products (ERA-Interim, CFSR, JRA-55, and NCEP–NCAR) (shading) and tropical cyclone genesis locations (dots) from IBTrACS for the period 1979–2008. Averaging regions used throughout the study are indicated by boxes in both panels.

Fig. 1.

JJAS EKE at (a) 850 and (b) 700 hPa from an average of four reanalysis products (ERA-Interim, CFSR, JRA-55, and NCEP–NCAR) (shading) and tropical cyclone genesis locations (dots) from IBTrACS for the period 1979–2008. Averaging regions used throughout the study are indicated by boxes in both panels.

AEW activity over Africa has a large impact on local (Sahel rainfall) and downstream (TCs) weather and climate. Thus, it is vital that we understand the ability of climate models to represent AEWs. This study aims to further our understanding of how AEWs are simulated in GCMs and to identify how these biases relate to future projections of Sahel rainfall and TC activity.

2. Data and methodology

a. Observations and reanalysis

Regions of interest within West Africa used in this study are identified in Fig. 1. They represent the locations of the northern (15°–25°N) and southern (5°–15°N) AEW tracks as well as the westward propagation of AEWs from land (0°–10°E) to the coast (10°–20°W). Also shown is the domain used for the whole West African region (boxes in Fig. 1b). As in many previous studies (e.g., Leroux et al. 2010), AEW activity is approximated using EKE as follows:

 
formula

where primes indicate 2–10-day filtered anomalies. Daily u and υ data at 850 and 700 hPa are used as a measure of low- and midlevel AEW activity and are obtained from four reanalysis products for 1979–2008: ERA-Interim (0.75°; Simmons et al. 2007), Climate Forecast System Reanalysis (CFSR; 0.5°; Saha et al. 2010), Japanese 55-year Reanalysis Project (JRA-55; 1.25°; Kobayashi et al. 2015), and NCEP–NCAR (2.5°; Kalnay et al. 1996). In addition, EKE at 850 hPa has been shown to be important for tropical cyclogenesis (Gray 1968; Thorncroft and Hodges 2001; Hopsch et al. 2007). Rainfall data are obtained from three observational datasets for 1979–2008: the Global Precipitation Climatology Project (GPCP) version 2.2 (2.5°; Adler et al. 2003), the CPC Merged Analysis of Precipitation (CMAP; 2.5°; Xie and Arkin 1997), and land-only CRU TS3.21 (0.5°; Harris et al. 2014).

b. CMIP5 model output

Daily winds at 850 and 700 hPa and monthly precipitation were obtained from multiple CMIP5 models (see Table 1 for a brief description of each model). We first investigate the simulation of AEWs in 28 models from the observed SST-forced atmosphere and land-only (AMIP) runs. As the well-known southeast Atlantic warm SST bias is present in CMIP5 simulations (Richter et al. 2014) and impacts the WAM (Roehrig et al. 2013), we examine the AMIP simulations to help establish the impact of SST bias on AEW characteristics. In addition, we examine 28 models from the historical and future projection (RCP8.5) simulations. Output from AMIP models is available from 1979 to 2008 and historical model output is used from 1979 to 2005. Changes in future AEW properties are established using differences between 2080–2100 RCP8.5 output and 1980–2000 historical output.

Table 1.

List of CMIP5 models that were used in this study along with horizontal resolution and availability of AMIP and historical simulations. Only one ensemble member per model was used. Further model details can be found in the indicated references and at the PCMDI website (http://www-pcmdi.llnl.gov; see www.ametsoc.org/PubsAcronymList for full model names). A single asterisk indicates models selected as one of the five lowest-resolution models and a double asterisk indicates models selected as one of the five highest-resolution models. Subscript numbers correspond to points in the Taylor diagrams in Figs. 3 and 4.

List of CMIP5 models that were used in this study along with horizontal resolution and availability of AMIP and historical simulations. Only one ensemble member per model was used. Further model details can be found in the indicated references and at the PCMDI website (http://www-pcmdi.llnl.gov; see www.ametsoc.org/PubsAcronymList for full model names). A single asterisk indicates models selected as one of the five lowest-resolution models and a double asterisk indicates models selected as one of the five highest-resolution models. Subscript numbers correspond to points in the Taylor diagrams in Figs. 3 and 4.
List of CMIP5 models that were used in this study along with horizontal resolution and availability of AMIP and historical simulations. Only one ensemble member per model was used. Further model details can be found in the indicated references and at the PCMDI website (http://www-pcmdi.llnl.gov; see www.ametsoc.org/PubsAcronymList for full model names). A single asterisk indicates models selected as one of the five lowest-resolution models and a double asterisk indicates models selected as one of the five highest-resolution models. Subscript numbers correspond to points in the Taylor diagrams in Figs. 3 and 4.

As is commonly done in model comparison studies, all data (model, observation, and reanalysis) is regridded to a common 2° horizontal resolution, which is approximately the average resolution of the models used. There is potential for this regridding to impact the results, but we do not attempt to quantify that impact in this study. Results are also presented from a subset of historical and future simulations based on horizontal model resolution. The five highest- and lowest-resolution models were selected for each group. When multiple models with the same resolution fell into either category, models were selected based on availability of rainfall data in the RCP8.5 models and vertical resolution. The selected models are identified in Table 1.

3. Simulation of AEWs and rainfall

Biases between the AMIP multimodel mean, multireanalysis JJAS EKE, and multiobservation JJAS rainfall are shown in Figs. 2a–c. The models produce too much EKE at both levels in the central and southern portion of West Africa (south of 20°N) and too little EKE to the north, with the largest biases over land at low levels and over the ocean at midlevels. A secondary region of underestimation by the AMIP models at midlevels (Fig. 2b) is evident to the south of the southern AEW track, along the coast of the Gulf of Guinea. The AMIP model underestimation (Figs. 2b,e) of the 700-hPa EKE maximum over the eastern Atlantic at 20°N (Fig. 1b) is likely a result of reduced midlatitude synoptic activity in the models and not AEW biases, as suggested by Mekonnen et al. (2006). In conjunction with the overestimation of EKE, AMIP models underestimate rainfall across West Africa (Fig. 2c).

Fig. 2.

JJAS differences between the CMIP5 multimodel means and multireanalysis/observation means (see text for details) for (a),(b),(d),(e) EKE (m2 s−2) and (c),(f) precipitation (mm day−1). EKE is shown at 850 hPa in (a) and (d) and at 700 hPa in (b) and (e). Differences from the (top) AMIP multimodel mean (1979–2008) and (bottom) historical multimodel mean (1979–2005) are shown.

Fig. 2.

JJAS differences between the CMIP5 multimodel means and multireanalysis/observation means (see text for details) for (a),(b),(d),(e) EKE (m2 s−2) and (c),(f) precipitation (mm day−1). EKE is shown at 850 hPa in (a) and (d) and at 700 hPa in (b) and (e). Differences from the (top) AMIP multimodel mean (1979–2008) and (bottom) historical multimodel mean (1979–2005) are shown.

The underestimation of Sahel rainfall in fully coupled climate models is often attributed to the warm SST bias in the Gulf of Guinea and southward shift of the monsoon (e.g., Roehrig et al. 2013). However, the underestimation of rainfall in the AMIP simulations indicates the presence of errors in the atmospheric and/or land component of the models. The excessive EKE activity and reduced precipitation suggests that the simulated AEWs are too strong and dry compared with reality and/or the models are unable to resolve the two different AEW tracks, and the dry northern AEWs are merged with the southern AEWs.

The biases in historical simulations are shown in Figs. 2d–f and the results are remarkably similar to the AMIP simulations. The differences between models and observations/reanalysis are larger in the historical models at low levels and over the ocean, but the same dipole pattern is evident in the EKE at both levels (Figs. 2d,e) and a dry bias is again clear across the Sahel (Fig. 2e). The major difference is the enhanced precipitation over the Guinea coast and Gulf of Guinea in the historical simulations. The large rainfall overestimation is due to the warm SST bias and therefore is not seen in the AMIP simulations. Historical and AMIP model differences in rainfall biases are also evident in the eastern Sahel, which may play a role in the triggering of AEWs (e.g., Thorncroft et al. 2008) but is not the focus of this study. Thus, we can conclude that the enhanced EKE and weak precipitation in CMIP5 simulations are not due to SST errors (although these may exacerbate the biases) but likely due to errors in the AEW forcing; maintenance, including representation of the AEJ; and feedbacks with convection in the atmospheric component of the models.

While Fig. 2 shows the biases in the multimodel ensemble mean, it is important to assess the spread of the models’ ability to simulate AEWs. This is illustrated by Taylor diagrams of the spatial distribution of JJAS EKE at 700 and 850 hPa over West Africa and the eastern Atlantic (dashed box in Fig. 1b) in Fig. 3. The reference dataset used is the multireanalysis mean. The huge spread in the ability of the individual models is clear at both levels, with the largest spread evident at low levels. Both AMIP and historical simulations show similar distributions across the Taylor diagram space, with spatial correlations ranging from near zero to above 0.95 and normalized standard deviations from near 0.1 to over 6. At both levels, the AMIP and historical multimodel means are closer to each other than to the multireanalysis reference, and despite the larger spread at low levels, the multimodel means have a more similar spatial variance to the multireanalysis mean than at midlevels but the spatial correlation is lower. Also shown in Fig. 3 are the four individual reanalysis products. The lowest-resolution NCEP–NCAR reanalysis tends to be more similar to the CMIP5 multimodel mean than the three higher-resolution and more modern reanalysis products. The spread in the EKE from the reanalysis is much larger than the spread in the three observational rainfall datasets (Fig. 4), highlighting the challenge of assessing model performance in West Africa when the spread in the reanalyzed conditions is so large. Also evident in the Taylor diagram for precipitation (Fig. 4) is the lower spread in the models, the higher correlation between models, and the multiobservation mean (no correlation value is below 0.7), and the higher skill of the multimodel ensemble mean (for both AMIP and historical simulations).

Fig. 3.

Taylor diagram representing the spatial variation of EKE at (right) 850 and (left) 700 hPa. All models from the AMIP (circles, blue) and historical (circles, red) simulations are shown in addition to the multimodel means (triangles). Four reanalysis products are plotted (stars) and the multireanalysis mean is used as the reference. Model values outside the range of the Taylor diagram are indicated below each panel with associated values. Area used is indicated by the dashed box in Fig. 1b. Individual models correspond to numbers and can be identified using Table 1. Note that a given number does not necessarily correspond to the same model for the AMIP and historical values in the Taylor diagram.

Fig. 3.

Taylor diagram representing the spatial variation of EKE at (right) 850 and (left) 700 hPa. All models from the AMIP (circles, blue) and historical (circles, red) simulations are shown in addition to the multimodel means (triangles). Four reanalysis products are plotted (stars) and the multireanalysis mean is used as the reference. Model values outside the range of the Taylor diagram are indicated below each panel with associated values. Area used is indicated by the dashed box in Fig. 1b. Individual models correspond to numbers and can be identified using Table 1. Note that a given number does not necessarily correspond to the same model for the AMIP and historical values in the Taylor diagram.

Fig. 4.

As in Fig. 3, but for precipitation. Three different precipitation datasets (stars) are shown.

Fig. 4.

As in Fig. 3, but for precipitation. Three different precipitation datasets (stars) are shown.

Skinner and Diffenbaugh (2013) showed that in CMIP3 models, AEWs were incorrectly coupled with convection, and this is still evident in CMIP5 models when considering JJAS EKE and rainfall, as illustrated in Fig. 5. Figure 5 shows correlation values between JJAS EKE (at low and midlevels) and rainfall from all models, experiments, and all combinations of reanalysis and rainfall data as box-and-whisker diagrams. In observations, correlations are larger at 700 hPa, and all combinations of reanalysis EKE and observational rainfall are positive and significantly different from zero at a 95% significance level, indicating larger seasonal mean precipitation and increased EKE at 700 hPa. The spread is larger at 850 hPa, which is not surprising given Fig. 3, and is also larger in all model simulations. In AMIP simulations, at both levels all models simulate positive EKE–rainfall correlations with multimodel mean values larger than the observed mean, close to 0.55 at 850 and 700 hPa. In the historical simulations the mean correlations are lower than in AMIP simulations and are closer to observed values, particularly at 700 hPa. In simulations of the future, the JJAS EKE and rainfall mean correlations are reduced from the historical simulations, suggesting a weaker relationship between seasonal mean EKE and rainfall in the future. However, the spread in all simulations, and particularly the fully coupled simulations is extremely large, with several models simulating an unrealistic negative correlation between EKE and rainfall on seasonal time scales. It is also evident in Fig. 5 that the models in the mean sense simulate too strong of a correlation at 850 hPa compared to 700 hPa as, unlike observations, most models have little difference between EKE and rainfall correlations at low and midlevels. Although correlations are shown for the entire West African domain, the results are extremely similar to those when only the southern region is used.

Fig. 5.

Box-and-whisker diagrams showing correlations between JJAS EKE and precipitation in the West African domain (solid box, Fig. 1b) for AMIP, historical, and RCP8.5 simulations as well as reanalysis/observations. Dashed black line indicates the correlation value that is statistically different from zero at the 95% significance level.

Fig. 5.

Box-and-whisker diagrams showing correlations between JJAS EKE and precipitation in the West African domain (solid box, Fig. 1b) for AMIP, historical, and RCP8.5 simulations as well as reanalysis/observations. Dashed black line indicates the correlation value that is statistically different from zero at the 95% significance level.

As discussed previously, the number, strength, physical characteristics, and timing of AEWs reaching the Atlantic can impact TC variability. Figure 6a shows biases at 850 hPa by longitude throughout the annual cycle between the historical multimodel mean and multiobservations/reanalysis for the southern band because of its importance for Sahel rainfall and TC activity. Results at 700 hPa and AMIP simulations showed no significant differences when compared to the historical simulations in Fig. 6a (not shown). It is apparent from Fig. 6a that over land EKE is overestimated between April and November and over the ocean EKE is underestimated by the models throughput the year. The sharp transition from over- to underestimation at the coast is distinct. We postulate that the large negative bias over the ocean that peaks in September is due to both the lack of realistic TCs in the models and the failure of the models to represent the reinvigoration of AEWs at the coast. Global climate models are able to produce systems with similar characteristics to observed TCs in the North Atlantic but tend to be larger and weaker than observed in low-resolution (e.g., Camargo et al. 2005) models and more realistic in high-resolution models (e.g., Zhao et al. 2009). As noted by Camargo (2013), many CMIP5 models have problems representing TC activity in the North Atlantic, even if they are able to capture TCs in other locations (the Indian Ocean and western Pacific). This lack of TC activity is consistent with reduced EKE over the Atlantic.

Fig. 6.

Hovmöller diagrams of historical model biases from the multireanalysis mean 850-hPa EKE (color shading) and multiobservation precipitation (contours, −5 to 5 mm day−1 with a 1 mm day−1 interval) averaged over the southern region (5°–15°N). Multimodel means are constructed using (a) all historical models, (b) the five highest-resolution models, and (c) the five lowest-resolution models. Green line indicates the approximate location of the African coast.

Fig. 6.

Hovmöller diagrams of historical model biases from the multireanalysis mean 850-hPa EKE (color shading) and multiobservation precipitation (contours, −5 to 5 mm day−1 with a 1 mm day−1 interval) averaged over the southern region (5°–15°N). Multimodel means are constructed using (a) all historical models, (b) the five highest-resolution models, and (c) the five lowest-resolution models. Green line indicates the approximate location of the African coast.

Also shown in Fig. 6 are the biases from the five highest- (Fig. 6b) and five lowest- (Fig. 6b) resolution historical models. The lowest-resolution models have much larger biases, especially over land, than the higher-resolution models, and the full multimodel mean and thus the transition from land to ocean at the West African coast is much more apparent in the lower-resolution models. In addition, it is evident from Fig. 6 that a negative rainfall bias exists at the coast in the region of the Guinea Highlands in both high- and low-resolution simulations, but the bias is larger in lower-resolution simulations. Several studies have suggested that the coast of West Africa plays an important role in the structure and intensity of AEWs (Berry and Thorncroft 2005; Ventrice et al. 2012; Janiga and Thorncroft 2013). For example, Ventrice et al. (2012) showed that the coastal Guinea Highlands trigger convection and generate potential vorticity that causes a strengthening of AEWs as they propagate into the Atlantic. The inability of the models to simulate convection over the Guinea Highlands (Figs. 2c,f and 6) is a cause of the AEWs rapidly dissipating (or failing to reinvigorate) at the coast in the southern band. This is consistent with Daloz et al. (2012), who found that in CMIP3 simulations the models with high rainfall over the Guinea Highlands region were associated with high AEW activity in the main development region and more TCs in the Atlantic.

The degraded simulation by the low-resolution models suggests that the representation of topography is important for the reinvigoration of AEWs and may impact the ability of models to simulate AEWs propagating into the Atlantic. The topography of North Africa is shown in Fig. 7c, with the Guinea Highlands located between 8° and 12°N close to the coast of West Africa and exceeding 800 m in elevation. Topography at 2.8° and 1.1°, representative of the low- and high-resolution simulations, is shown in Figs. 7a and 7b, respectively. The lack of convection and reinvigoration of AEWs in the low-resolution models is consistent with the Guinea Highlands not being present in the topography at low resolutions. At high resolutions, the Guinea Highlands are represented, but they are lower than in reality and thus some biases are still observed.

Fig. 7.

Topography over the West African region. Topography from a representative (a) low-resolution and (b) high-resolution CMIP5 model is shown along with (c) observed topography from the ETOPO1 global relief model (Amante and Eakins 2009).

Fig. 7.

Topography over the West African region. Topography from a representative (a) low-resolution and (b) high-resolution CMIP5 model is shown along with (c) observed topography from the ETOPO1 global relief model (Amante and Eakins 2009).

4. Future projections of AEW activity

It is likely that Sahel rainfall and TC activity will be impacted by future changes in AEW activity, but the large biases in simulations of AEWs that have been illustrated require future changes to be interpreted with caution. Figure 8 shows the fractional changes in low-level (the results at midlevel are similar and thus not shown) EKE for the northern (Figs. 8a,b) and southern (Figs. 8e,f) bands at the coast (Fig. 8, left) and over land (Fig. 8, right). These regions are shown by the boxes in Fig. 1a. Similar to Fig. 1a in Skinner and Diffenbaugh (2014), increased AEW activity is projected during JJAS in the northern region with smaller EKE changes in the southern region. The spread in model projections is also much smaller in the northern region. Fractional changes in EKE over the land in the northern region are the largest, with multimodel mean increases between June and October that peak near 30% in August, leading to a more sharply defined EKE annual cycle in the future.

Fig. 8.

Annual cycle differences between RCP8.5 (2080–2100) and historical (1980–2000) simulations. Differences are shown for 850-hPa (a),(b),(e),(f) EKE and (c),(d),(g),(h) precipitation. Averaging regions are shown in Fig. 1a, with results from the (left) coastal and (right) land regions. The northerly region (15°–25°N) is shown in the top four panels and the southerly region (5°–15°N) in the bottom four panels. Shading indicates plus/minus one standard deviation from the multimodel mean. Differences in EKE are measured as a fractional change relative to the mean annual cycle in the historical period (1980–2000), and precipitation values are shown in mm day−1.

Fig. 8.

Annual cycle differences between RCP8.5 (2080–2100) and historical (1980–2000) simulations. Differences are shown for 850-hPa (a),(b),(e),(f) EKE and (c),(d),(g),(h) precipitation. Averaging regions are shown in Fig. 1a, with results from the (left) coastal and (right) land regions. The northerly region (15°–25°N) is shown in the top four panels and the southerly region (5°–15°N) in the bottom four panels. Shading indicates plus/minus one standard deviation from the multimodel mean. Differences in EKE are measured as a fractional change relative to the mean annual cycle in the historical period (1980–2000), and precipitation values are shown in mm day−1.

Future projections in the southern region, although weaker, indicate a reduction (~10%) of EKE in May and June that could have significant impacts on the timing of TC activity in the future. However, because of the large spread of the projected changes, which may be in part due to the large biases in westward propagation of AEWs, caution must be used when interpreting future changes. However, Dwyer et al. (2015) show that any future delay in the beginning of the TC season is in part explained by mean changes in overall TC number that could be a result of fewer AEWs, as shown here. Also illustrated in Figs. 8c, 8d, 8g, and 8h is the future change in the annual cycle of rainfall at each location. The correlation between AEW activity and rainfall is evident, with rainfall showing spring and early summer reductions and late summer increases, particularly in the southern region, which receives significantly more rainfall than the northern band. This is consistent with previous results showing a similar change in the future annual cycle of Sahel rainfall in the CMIP5 models (Biasutti 2013).

As high-resolution models have reduced biases in AEWs and rainfall in the current climate as compared to low-resolution models, Fig. 9 shows projected changes in the annual cycle of 850 hPa EKE and rainfall at the four locations shown in Fig. 1a and consistent with Fig. 8 for the mean of the five high- and five low-resolution models. Future changes in AEWs and rainfall for the southern band are very different in high- (Figs. 9a,b) and low-resolution (Figs. 9c,d) simulations. The high-resolution models project a much larger increase in EKE (>30% in July) and rainfall (up to 0.8 mm day−1 in July) over land during summer than the full multimodel mean (Figs. 8f,h) while maintaining the reduction in EKE during May and June. This is in stark contrast to the projections from the low-resolution models over land (Fig. 9d) that project minimal EKE increases of less than 5% during July and August and rainfall increases of less than 0.5 mm day−1 during the same period.

Fig. 9.

Annual cycle differences in 850 hPa EKE (black, left scale) and precipitation (blue, right scale) between RCP8.5 (2080–2100) and historical (1980–2000) simulations (left) at the coast and (right) over land, with the northerly and southerly locations indicated in each figure by the dashed and solid lines, respectively. Shown are differences for the (a),(b) five highest- and (c),(d) five lowest-resolution models. Differences in EKE are measured as a fractional change relative to the mean annual cycle in the historical period (1980–2000), and precipitation values are shown in mm day−1. Averaging regions are shown in Fig. 1a.

Fig. 9.

Annual cycle differences in 850 hPa EKE (black, left scale) and precipitation (blue, right scale) between RCP8.5 (2080–2100) and historical (1980–2000) simulations (left) at the coast and (right) over land, with the northerly and southerly locations indicated in each figure by the dashed and solid lines, respectively. Shown are differences for the (a),(b) five highest- and (c),(d) five lowest-resolution models. Differences in EKE are measured as a fractional change relative to the mean annual cycle in the historical period (1980–2000), and precipitation values are shown in mm day−1. Averaging regions are shown in Fig. 1a.

The high-resolution models project small and more variable changes at the coast (Fig. 9a), indicating that the large changes in AEW activity over the land are not translated to the coast, as expected from Figs. 2, 6. We hypothesize that this is due to the inability of the models to realistically simulate the westward propagation of AEWs to the Atlantic as models fail to maintain and grow AEWs at the coast. The reduction in EKE and rainfall during May and June and an increase in July at the coast (Fig. 9a) is consistent with the land signal, although reduced in amplitude. As for the land, the coastal region has a different projection in the low-resolution models (Fig. 9c) with decreases in EKE (up to 10%) and rainfall (up to 0.5 mm day−1) between June and October. The spread, as measured by the standard deviation, is large for the projections from the high- and low-resolution models because of the selection of only five models in each group and the numerous other model differences besides resolution, such as parameterization schemes and dynamical cores.

Using EKE as a measure of AEW activity is well established. However, changes in EKE can be due to variations in the frequency or intensity (or a combination) of AEWs. To measure the number of AEWs occurring over JJAS, the 2–10-day filtered meridional wind at two base points (northern region 0°E, 20°N and southern region 0°, 10°N) was used. An AEW was recorded each time the filtered meridional wind exceeded a given threshold (1–4 m s−1, as shown in Table 2) during each JJAS. If the threshold was exceeded on consecutive days, only one wave was recorded. For the northern region, meridional wind at 850 hPa was used, and 700-hPa meridional wind was used in the southern region to reflect the location of the AEW maximum strength. The average number of AEWs in JJAS for each model was recorded for the historical simulation (1980–2000) and the RCP8.5 simulation (2080–2100) to establish the change in AEW frequency, and the threshold value was varied as an indication of AEW intensity changes.

Table 2.

Number of models simulating significant (at 95%) positive or negative changes in JJAS AEW numbers and the multimodel mean difference. Changes are between historical (1980–2000) and RCP85 (2080–2100) periods. Information is shown for 850 hPa (base point of 20°N, 0°) and 700 hPa (base point of 10°N, 0°) for different thresholds of AEW activity (1–4 m s−1).

Number of models simulating significant (at 95%) positive or negative changes in JJAS AEW numbers and the multimodel mean difference. Changes are between historical (1980–2000) and RCP85 (2080–2100) periods. Information is shown for 850 hPa (base point of 20°N, 0°) and 700 hPa (base point of 10°N, 0°) for different thresholds of AEW activity (1–4 m s−1).
Number of models simulating significant (at 95%) positive or negative changes in JJAS AEW numbers and the multimodel mean difference. Changes are between historical (1980–2000) and RCP85 (2080–2100) periods. Information is shown for 850 hPa (base point of 20°N, 0°) and 700 hPa (base point of 10°N, 0°) for different thresholds of AEW activity (1–4 m s−1).

The number of models projecting positive or negative changes in AEW numbers, as well as the mean change for the northern (850 hPa) and southern (700 hPa) land regions, are shown in Table 2. As expected from Fig. 8b, the northern region shows more consistent projections with the majority of models projecting increased numbers of AEWs in the future at all thresholds, especially at the highest thresholds. This result is consistent with Skinner and Diffenbaugh (2014), who showed that in the northern band more extreme AEWs were projected. The results are more varied in the southern band (Table 2), with similar numbers of models projecting increases and decreases to AEW numbers at multiple thresholds. The mean change in AEW numbers is much smaller than in the northern region, but the decrease at higher thresholds (3 and 4 m s−1) suggests that, in contrast to the northern region, the more extreme AEWs are not increasing in future projections.

5. Summary and conclusions

Future projections of AEW activity across West Africa may have important consequences for downstream TC activity. However, large biases exist in the simulation of AEWs in CMIP5 models. The models produce AEWs in the Sahel that are too strong and too dry in comparison to observations. The biases exist in both historical and AMIP models and thus are not just a consequence of the large Gulf of Guinea warm SST bias in historical simulations. Models from all experiments (AMIP, historical, and RCP8.5) fail to represent the increased seasonal correlation between EKE and rainfall. Effectively, the coupling between AEWs and convection is too strong at 850 hPa, with little improvement since CMIP3 (Skinner and Diffenbaugh 2013). Additional analysis of AEW structures to determine whether this is due to the inherent characteristics of the simulated AEWs or due to the merging of the tracks in the models is required to determine the source of these biases.

Perhaps the most crucial process for TC activity in the Atlantic is that CMIP5 models do not propagate AEWs across the coast and into the Atlantic with the same strength as seen in observations/reanalysis. The CMIP5 models simulate reduced convection over the Guinea Highlands in southern West Africa and thus are not able to provide additional potential vorticity input to reinvigorate the AEWs as they move over the coast. The degraded simulation of this westward propagation over the coast in the lowest-resolution simulations is consistent with the Guinea Highlands not being resolved at resolutions at and lower than 2.8°. We hypothesize that this will improve when models are able to resolve the Guinea Highlands and consequently generate more rainfall and diabatic heating at the coast, which reintensifies the AEWs and allows them to propagate further westward. At 1.1°, the Guinea Highlands are evident but they are not as high as observed, and thus rainfall and reinvigoration is still underestimated. However, there are many other differences between the high- and low-resolution model groups that may also contribute to simulation ability, such as convective parameterization and land surface schemes. Thus, simply improving the topography resolution would likely not produce a perfect simulation. To precisely diagnose the role of topography in AEW propagation into the Atlantic, controlled model experiments with only topography resolution and representation varying would need to be performed.

High- and low-resolution models produce widely disparate changes in the annual cycle of southern region AEWs and rainfall in the future. In high-resolution models (and the full multimodel ensemble) the annual cycle shows a reduction in early season AEW activity and rainfall in the southerly track, particularly in June. Over land, future projections show a similar reduction in early season AEWs and rainfall and a large increase in EKE between July and October that is consistent with rainfall projections in the Sahel (Biasutti 2013). There are many potential reasons why the higher-resolution models are producing larger changes in southern region AEWs in the future than the low-resolution models that require further investigation. The differences could be related to the simulation of meridional temperature gradients and feedbacks on the African easterly jet, the upstream triggering of convection in the eastern Sahel that may not be resolved well at low resolutions and thus not changing in future simulations, and differing diabatic feedbacks associated with moist waves in the southern region in high-resolution models. The convective parameterization could be a cause of the spread in the simulations at high resolution for this reason. It also needs to be established whether the CMIP5 models are simulating two distinct AEW tracks with different AEW characteristics.

While these results identify potential changes in the annual cycle of TC seeds entering the Atlantic, we suggest that caution must be taken because of the large spread in future projections and large biases in simulated AEWs and TCs (e.g., Camargo 2013). The results from this study suggest that as global climate models progress to increasingly higher resolution, their capability to simulate the westward propagation of AEWs into the Atlantic should increase (due to resolving the Guinea Highlands) and allow for more realistic interactions between AEWs and TCs, which also become more realistic at higher resolution (e.g., Zhao et al. 2009). Thus, there is a strong need to investigate the simulation of AEW structures and characteristics in global and regional (e.g., Crétat et al. 2015) climate models to further understand the role biases in AEW properties and characteristics may play in influencing future projections of TC activity and rainfall in the Sahel.

Acknowledgments

The authors thank three anonymous reviewers for thoughtful comments and suggestions that greatly strengthened the manuscript. The research was supported by NOAA Grant NA10OAR4310205 and start-up funds provided to EM by the University of Oklahoma. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and the climate modeling groups for producing and making model output available. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.

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Footnotes

This article is included in the In Honor of Peter J. Lamb special collection.